Abstract The identification of thousands of genetic variants associated with human health and disease through genome- wide association studies (GWAS) has kindled the hope of translating genetic findings into clinical and public health practices. In the next few years, exome and full genome sequence-based GWAS will continue to pro- pel the field of complex genetics. The success of GWAS, however, has been largely confined to populations of European descent. Understanding of disease etiology in minority populations remains limited, especially in populations with mixed continental ancestries such as African Americans and Hispanics who, paradoxically, suffer from disproportionate disease burdens. Chief among the barriers in filling this knowledge gap is the lack of large minority population cohorts, which are required to detect the myriad genes of modest effect underlying common, complex diseases. The problem is likely exacerbated as we moved towards sequencing- based association studies. The brute force solution to this problem, by establishing an adequately powered and well-phenotyped cohort for every minority population, and analyzing each population in isolation, is neither feasible nor efficient. New analytic strategies must be explored to improve the efficiencies of GWAS in minority populations. The long-term goals of this research are to develop novel quantitative methods for understanding the etiol- ogy of complex diseases in admixed populations, and to translate this knowledge into effective clinical and public health practices, thereby contributing to the elimination of ethnic health disparity. Th objective of this application is to develop statistical and computational methods whereby genome-wide information, such as genotype and sequencing data, can be used to estimate both shared and unique components of the genetic architectures between populations. This objective is met by pursing three Specific Aims: (1) characterize the overlap in genetic architecture between populations, (2) objectively assess the genetic contribution to ethnic health disparities in an admixed population, and (3) develop an approach for individual risk prediction in an under-represented ethnic group by adaptively assimilating information across populations. The proposed re- search is innovative because it promotes and enables a transition toward a multi-ethnic paradigm in GWAS, in which the large, existing and underused resource of European GWAS results can be judiciously leveraged to accelerate disease studies in minority populations. This research is significant because it will provide an integrated understanding of the genetic architecture of complex traits in all human populations, and at the same time identify where ethnicity-specific prevention and intervention strategies are most needed.